JUISI, Vol. 02, No. 02, Agustus 2016 41 Improving Data Warehouse Performance Using Filesystem Technology with GZIP, LZJB and ZLE Compression Andre Wenas1, Suharjito2 Abstract— Data warehouse application is commonly used by The amount of data that must be processed increases many corporations as an analytical platform to develop exponentially, so it takes a certain method that can store marketing strategy and give advantage edge to their data more efficiently over a long period. One method for business. Many times, data warehouse platform need to reducing the amount of data storage is to use a manage huge amount of data as it needs to extract, load and compression (Roth and Van Horn, 1993). Compression transform enterprise data from many sources. To improve performance of data warehouse system, compression has technology is widely used to store individual files and is been used in various ways, both at database layer or at used to store data in a relational database in corporations. filesystem layer. Compression brings an other benefit as it As data warehouse is one of the important applications reduce storage capacity therefore reduce cost. However for commercial business, therefore improving compression also add data processing that may impact to performance and reducing storage capacity will bring overall application response time. In this research, three significant benefits for the business. For example, with a different compression algorithms which are zle, lzjb and compression ratio of 2:1, businesses will spend half of gzip were tested on data warehouse filesystem to understand their IT budget to meet the same requirements. It will help the performance impact and the capacity saving benefit. directly to the profitability of the company. At the same Using swingbench as the benchmark tool and oracle database 12c, it shows that zle is the best compression time, improving the performance will give better algoritm with performance improvement of 92%, follows by customer satisfaction [2]. lzjb with 78% performance improvement and gzip with In this study, we want to see whether data compression 53% improvement. In terms of compression ratio, gzip can can increase performance of data warehouse using the deliver the highest with 3.31 compression ratio, follows by latest hardware technology. We need to observe how lzjb with 2.17 compression ratio and zle with 1.55 much increase in performance, impact on CPU overhead compression ratio. AW. and saving compression ratio. There are few option where to implement Keywords: data warehouse; performance; compression; compression, compression can be done at the database zle; lzjb; gzip layer or at the filesystem layer. At filesystem layer that can perform compression transparently, filesystem will perform compression processing in memory cache that I. INTRODUCTION can reduce the overhead of compression and uncompress processes, and accelerate the writing of the data that has The complexity of the modern business world been compressed into a disk [3]. contributes to the complexity of today's IT infrastructure. In this paper, we are experimenting to use filesystem Growth, mergers, acquisitions and long-term IT compression with zle, lzjb and gzip compression to investment has created the technology landscape is improve data warehouse performance. A simulated data characterized by silos of information held on different warehouse benchmark was conducted to measure the systems in different departments, subsidiaries and performance improvement, compression ratio, response geographies. More data captured and stored by the time and CPU overhead against filesystem without business now than ever before. The explosion of data and compression and oracle database ACO compression. data processing applications continue to be a challenge. Compression refers to the process of reducing the For example, Infosys, a multinational company in India, amount of data of the original information. Database the data warehouse system grown by 45% with compression is widely used in data management to performance improvement and to save storage space. Data compression is the art of finding a short description for 1Information Technology Graduate Program, Bina the long string. each compression algorithm can be Nusantara University Information Technology Faculty,e-mail: decomposed into zero or more transformations, model, [email protected] and program [4]. The goal of data compression is to 2Information Technology Graduate Program, Bina reduce the redundancy of data stored or communicated, Nusantara University Information Technology Faculty,e-mail: thereby increasing the effective data density. Data [email protected] compression has important applications in the areas of file storage and distributed systems [2]. transaction volume growing by 110% annually [1]. Andre Wenas: Improving Data Warehouse Performance… ISSN: 2460-1306 42 JUISI, Vol. 02, No. 02, Agustus 2016 Data compression is often referred to as coding, in [6] Entropy encoding: Compression which the coding is a very general term that covers any Huffman dan ratio: LZW specific data representations that meet specific needs. Shannon-Fano. provides best Information theory is defined as the study of efficient Directory ratio, follows by coding and its consequences, such as the transmission encoding: LZW Huffman and speed and probability of error. Data compression can be from Lempel-ziv Shannon-Fano. regarded as a branch of information theory in which the family Huffman and main objective is to minimize the amount of data Shannon-Fano transferred. A simple characterization data compression is compression is that it involves changing a string of characters in multiple faster than LZW, representations (such as ASCII) into a new string (bits, whereby for for example) which contains the same information but decompression whose length is as small as possible [5]. Lossless LZW is faster than compression algorithm for text can be divided into 2 Shannon-Fano. groups: compression algorithm based on statistical [7] Entropy/statistic Compression entropy encoding for example Shannon-Fano coding, encoding: ratio: Huffman coding, Adaptive coding and compression Shannon-Fano Directory algorithms based encoding directory such as Lempel-Ziv, Coding, Huffman encoding Lempel- LZ77 and LZ78 [6] [7]. coding, Adaptive ziv has better While data compression have significant benefits for coding, Run compression ratio businesses, also inherited a problem: the overhead of Length encoding compare to compression / decompression [3]. Overhead issued by the (RLE), Arithmetic entropy encoding process to compress data and expansion process to encoding. family. recover the original data is one of the most serious Directory weaknesses of the data compression. For some encoding: Lempel- applications, this overhead can be large enough to prevent Ziv family, consideration for the use of data compression. On the LZ77 dan LZ78 other hand, the use of compression will reduce the amount of data that must be read or written. Reduction of This research will use three types of algorithms are amount of data that must be read or written will reduce gzip, lzjb and zle. Here is a description of the intended the number of transactions to storage IO significantly. compression: Because IO transactions are much slower than data - ZLE (Zero Length Encoding) algorithm is a processing, the use of compression will improve compression algorithm that eliminates or compress the performance of the database significantly. data with zero value (zero). With zero data compression In four decades we have witnessed an exponential saves storage and accelerate data transmission. This growth in computing power. Chip microprocessor algorithm is derived from the algorithm RLE (Run Length technology used billions of transistors, including multiple Encoding) that belong to the entropy encoding although processor cores, running at speeds measured in gigahertz, some are classified RLE encoding as a separate group. and provides more than 4 million times the performance RLE algorithm is very easy to implement, therefore it has of the original [8]. On the other hand, the basic a very fast performance [10]. architecture of the disk drive has not changed much since - LZJB algorithm (Lempel Ziv Jeff Bonwick) is a it was introduced. The growth curve relative performance compression algorithm invented by Jeff Bonwick to (IOPS) of about 25 percent, less than the rate of increase perform data compression on ZFS filesystem. This disk capacity [9]. Very likely that with the latest algorithm is based on the method LZRW1 algorithm that technology micro-processor, the CPU is much faster to also included family Lempel-Ziv compression algorithm process compression / decompression that will help to by encoding the directory. LZRW algorithms designed to improve overall performance. get the performance as soon as possible so it is suitable The effectiveness of the technique of compression for compression filesystem [10]. algorithms is determined by three things: the compression - GZIP algorithm is a compression algorithm invented by ratio to see the savings capacity, performance Jean Loup gaily and Mark Adler in 1992 and is widely compression and de-compression performance. used in the open-source software, especially Linux. This Comparison of different lossless compression algorithms algorithm is based on the DEFLATE algorithm is a is listed on table 1. combination of LZ77 (including family Lempel-Ziv algorithms – directory encoding) and Huffman coding